Research on the application of artificial intelligence in computer network technology in the era of big data

Abstract

SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.